NDLI: A generalized thresholding algorithm of pedestrian segmentation for far-infrared images

Content Provider	IEEE Xplore Digital Library
Author	Qiong Liu Jiajun Zhuang
Copyright Year	2012
Description	Author affiliation: School of Software Engineering, School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China (Qiong Liu; Jiajun Zhuang)
Abstract	Designing a robust and efficient thresholding algorithm for far-infrared (FIR) images under various imaging conditions is one of critical technologies. The existing algorithms are difficult to deal with the images corrupted by noise, if a predefined filter is not used. However, it is difficult to define an appropriate filter beforehand because some prior knowledge about image noise is required. To solve this problem, an improved fast generalized fuzzy c-means (IFGFCM) is proposed to reconstruct a filtered image first regardless of the type of image noise. A novel adaptive thresholding algorithm combining IFGFCM with clustering centers analysis is then used to segment pedestrians from FIR images automatically. Experiments performed on a set of FIR images show that, compared with three other algorithms, the segmentation effectiveness of the thresholding algorithm is more consistent with the ground truth, and the resulting misclassification rate is less than 2%.
Starting Page	338
Ending Page	343
File Size	1311823
Page Count	6
File Format	PDF
ISBN	9781457717765
e-ISBN	9781457717758
DOI	10.1109/IST.2012.6295515
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-16
Publisher Place	United Kingdom
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Finite impulse response filter Clustering algorithms Algorithm design and analysis Noise Image segmentation Filtering algorithms Histograms spatial context pedestrian segmentation far-infrared images thresholding clustering centers analysis fuzzy c-means
Content Type	Text
Resource Type	Article

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